Управление данными подвижных объектов с MobilityDB

MobilityDB is a moving object database extension to PostgreSQL and PostGIS. It has types and functions for storing an querying geospatial trajectories, as first class citizens. The main type is called tgeompoint (temporal geometry point). It represents a complete movement track of a geometry point, such as a car, a bird, or a person. The function speed(tgeompoint) computes the time varying speed of the object, as a tfloat (temporal float). Similar to these examples, MobilityDB has 6 temporal types, and over 300 functions. As such, it is a function-rich platform for Mobility Data Management.

In this tutorial you will:

• learn about moving object databases
• write MobilityDB SQL queries and explore a database of geospatial trajectories
• walk through the different type, indexes, and functions of MobilityDB.

Оптимизация производительности PostgreSQL

PostgreSQL is one of the leading open-source databases. Out of the box, the default PostgreSQL configuration is not tuned for any particular workload. The default configuration is designed in such a way that PostgreSQL can run on any system using minimum resources. Consequently, a default installation of PostgreSQL does not give optimum performance on the high-performance machine because it is set up to use all available resources. PostgreSQL provides mechanisms that allow you to tune your database according to your workload and machine specification. Outside of PostgreSQL, though, we can tune the Linux kernel to allow the database load to work optimally. In this talk, we will learn how to tune some of the PostgreSQL’s parameters, and we will see the effect of that tuning, but we will focus on demonstrating how to tune Linux for better Postgres performance. As there are so many Linux kernel parameters that can be tuned to improve the performance of PostgreSQL, I will also share the results of benchmarks obtained when tuning some of the Linux parameters.

Ускорение быстрого текстового поиска с помощью индекса RUM

Fast text search queries can be made even faster with indexing on lexemes inside compound records of tsvector format. RUM index is an open-source PostgreSQL extension. It represents a big improvement of GIN index and it can index lexemes with additional information e.g. tsvector lexeme weight-mark. So it can support tsvector capabilities more.

Until recently it was needed to recheck results of weight-containing queries by table. My modification (2020) is to make the processing of this kind of queries index-only and therefore much faster.

Also, I will describe and provide benchmarks for different usage cases for fast text search. We'll see how RUM index can improve the performance and compare it with PostgreSQL internal GIN index.

Сетевой фильтр PostgreSQL для EnvoyProxy

How do you monitor Postgres? What information can you get out of it, and to what degree does this information help to troubleshoot operational issues? What if you want/need to log all the queries? That may bring heavy trafficked databases down.

At OnGres we’re obsessed with improving PostgreSQL’s observability. So we worked together with Tetrate folks on an Envoy’s Network Filter extension for PostgreSQL, to provide and extend observability of the traffic inout a cluster infrastructure. This extension is public and open source. You can use it anywhere you use Envoy. It allows you to capture automated metrics and to debug network traffic. This talk will be a technical deep-dive into PostgreSQL’s protocol decoding, Envoy proxy filters and will cover all the capabilities of the tool and its usage and deployment in any environment.

Resources:

• https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/other_protocols/postgres#arch-overview-postgres
• https://www.cncf.io/blog/2020/08/13/envoy-1-15-introduces-a-new-postgres-extension-with-monitoring-support/